About us…

Floris van Ogtrop - Unit Coordinator

• Room 306, Level 3, Biomedical Building, Australian Technology Park, Eveleigh
• Ph: 02 8627 1024
• Email: floris.vanogtrop@sydney.edu.au

About ENVX1002

Delivery format

All lectures and tutorials are held in ABS Lecture Theatre 1130. Lab sessions are held in the Biomedical Building, Australian Technology Park, Eveleigh.

• Lectures (recorded): deliver content, provide context, and introduce new concepts
• Tutorials (recorded): practice and apply concepts from lectures, prep for labs
• Labs: hands-on practice with R and data analysis, with demonstrators to help you

Timetable

Schedule at a glance…

sequenceDiagram
  participant M as Mon
  participant T as Tue
  participant W as Wed
  participant Th as Thu
  participant F as Fri
  participant S as Sat
  participant Su as Sun

  Note over M,T: Lectures (recorded) - ABS LT 1130
  Note over T: Tutorial (recorded) - ABS LT 1130
  Note over T,Th: Lab Sessions - Biomedical Building
  Th->>+Su: Self-revision, pick ONE day (encouraged)

sequenceDiagram
  participant M as Mon
  participant T as Tue
  participant W as Wed
  participant Th as Thu
  participant F as Fri
  participant S as Sat
  participant Su as Sun

  Note over M,T: Lectures (recorded) - ABS LT 1130
  Note over T: Tutorial (recorded) - ABS LT 1130
  Note over T,Th: Lab Sessions - Biomedical Building
  Th->>+Su: Self-revision, pick ONE day (encouraged)

Resources

• Canvas (of course)
• ENVX-resources – GitHub repository for our open-source materials
• Ed Discussion – main platform for ANNOUNCEMENTS and Q&A

Where are the Labs?

• Lab sessions include extra time (30 minutes) for travel – already programmed in the timetable (so clashes are avoided)
• A free shuttle service is available between campus and the labs, but the schedule is very limited
• Take advantage of the new community access gates at Redfern Station: saves 5 minutes

Topic outline

• Week 01 - Data: Reproducible science
• Week 02 - Data: Introduction to statistical programming
• Week 03 - Data: Exploring and visualising data
• Week 04 - Data: The Central Limit Theorem

Assessments

# calculate this year's year number
library(lubridate)
year <- year(Sys.Date())
address <- paste0(
  "https://www.sydney.edu.au/units/ENVX1002/",
  year,
  "-S1C-ND-CC"
)

The most up to date (and slightly more comprehensive) information for 2025 is here. In a nutshell:

• Week 3: The early feedback quiz is a chance for us to gauge your understanding and provide feedback
• Week 8: Coding and data skills evaluation covers R data manipulation and analysis
• Final exam will NOT require you to write or interpret code – focus on understanding concepts and interpreting results

Baby steps…

• This unit is designed for beginners - no prior statistics or programming required
• We start with basics – pace increases after week 4
• Focus on understanding concepts first, then tools
• We provide plenty of support – more on this later

Our tech stack

1. MS Excel – for data entry and basic analysis
2. R – a programming language for data analysis
3. RStudio – an integrated development environment (IDE) for R
4. Quarto (Markdown) – a key platform for reproducible reports and documents
5. GitHub Copilot – AI-powered code completion tool. Optional, but highly recommended

MS Excel

• Widely used for data entry and basic analysis
• A standard tool in many industries, including science, often to store data
• Can be a useful complement to R for data cleaning and simple calculations
• A stepping stone to more advanced tools?

R

Why R?

1. Built for beginners
2. Makes your work reproducible
3. Powerful yet accessible

• Importantly – the skills you learn are highly transferable to other tools and languages.
• Most easily integrated with generative AI tools – more on this soon
• Well-documented and discussed online (so you can find help easily)

RStudio

• NOT the same as R – it’s an integrated development environment (IDE)
• Runs R (…and Python, and SQL, and more)
• Makes it easier to write and run R code by providing a significantly more user-friendly interface

Starting with R

• It’s normal to feel overwhelmed at first
• We’ll learn step by step
• Practice is key - a little bit each day helps
• Don’t hesitate to ask questions!

Satisfying when it works

# Load required packages
library(gapminder) # Dataset of country statistics over time
library(gganimate) # For creating animations in ggplot
library(tidyverse) # Collection of data science packages

# Create an animated plot showing how life expectancy relates to GDP
# across different continents over time
ggplot(
  gapminder,
  aes(gdpPercap, lifeExp, # GDP per capita vs life expectancy
    size = pop, # Point size represents population
    colour = country
  )
) + # Each country gets its own color
  geom_point(
    alpha = 0.7, # Semi-transparent points
    show.legend = FALSE
  ) + # Hide legend for cleaner look
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) + # Set min/max point sizes
  scale_x_log10() + # Log scale for GDP (wide range)
  facet_wrap(~continent) + # Separate plot for each continent
  labs(
    title = "Year: {frame_time}",
    x = "GDP per capita",
    y = "Life expectancy"
  ) +
  transition_time(year) + # Animate through years
  ease_aes("linear") # Smooth transitions

Quarto

• Marjority of our resources are built using Quarto – a markdown-based document format that you will learn to use in this unit
  • Lecture slides
  • Tutorials
  • Lab exercises
• Quarto makes everything reproducible - what does it mean?
• Free and open source, available on the ENVX resources GitHub repository – re-use and modify as you wish (but follow CC BY 4.0)

## Quarto

- Marjority of our resources are built using [**Quarto**](https://quarto.org/) -- a markdown-based document format that **you will learn to use** in this unit
  - Lecture slides
  - Tutorials
  - Lab exercises
- Quarto  makes everything **reproducible** - what does it mean?
- Free and open source, available on the [ENVX resources](https://github.com/ENVX-resources) GitHub repository -- re-use and modify as you wish (but follow [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))

R, RStudio, Quarto!?

• Again, it’s normal to feel overwhelmed at first
• These technologies are complementary – everything is implemented in RStudio
• The tutorials and labs will guide you through the process

GitHub Copilot

• Slightly different from your usual AI tools
• Acts as a pair programmer – suggests code as you type, based on context
• Double-edged sword – can be a crutch if you rely on it too much
• We will start using this tool in Week 4

Seek help early

• You are not alone, and you need to learn to ask for help
• We provide a LOT of support in various forms:
  • Face-to-face (in a group setting): tutorials, labs
  • Face-to-face (one-on-one): consultations – book a time with us (email)
  • Online (collaborative): Ed for general questions
  • Online (private): use private posts on Ed
• From time to time, we will organise drop-in sessions for additional help

NOTE: We cannot help you if you don’t ask!